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Documentation Index

Fetch the complete documentation index at: https://docs.woodwide.ai/llms.txt

Use this file to discover all available pages before exploring further.

Train a model on any dataset by specifying a model type. Retrain on new data to create a new version while keeping the same model ID.

Train Model

Train a new model on a dataset.

Retrain Model

Create a new version of an existing model with fresh data.

Get Model

Fetch details and status for a specific model.

List Models

Retrieve all models in your account.
See Capabilities for details on each model type.

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Selecting Input Columns

By default, all columns in the dataset are used as input features (except the target column for prediction models, which is used only as the label). You can optionally pass input_columns when training to restrict which columns the model uses:
Python
resp = requests.post(
    f"{base_url}/models/train",
    headers=headers,
    json={
        "model_name": "my_model",
        "model_type": "prediction",
        "dataset_id": dataset_id,
        "label_column": "target",
        "input_columns": ["age", "income", "region"],
    },
)